JP4906505B2 - Expression profile algorithms and tests for cancer diagnosis - Google Patents

Description

(Field of Invention)
The present invention provides a non-invasive quantitative test for prognostic decisions in cancer patients. This test relies on tumor level measurements of specific messenger RNA (mRNA) or corresponding gene expression products. These mRNA or protein levels are substituted into a polynomial (algorithm) that yields a numerical score that indicates the risk of relapse (recurrence score) or likelihood of patient response to treatment (response score).

(Description of related technology)
There is a need for clinical trials that can help oncologists make well-informed treatment decisions. One of the most basic decisions faced by oncologists in daily practice is the decision to use or postpone specific patients with chemotherapeutic agents. Current therapeutics for cancer are generally accompanied by considerable toxicity but are not as potent. Therefore, it is highly desirable to pre-determine that a patient (after resection of the primary tumor) may have metastatic recurrence. If there was a reliable way to obtain this information, adjuvant chemotherapy may be selected for high-risk patients, and patients not likely to have cancer recurrence will be immune to adverse events associated with chemotherapy. The necessary exposure could have been avoided. Similarly, before subjecting a patient (either before or after resection of the primary tumor) to a particular therapy, it is desirable to know whether the patient is likely to respond to such treatment. For patients who are not likely to respond to a particular therapy (eg, treatment with a particular chemotherapeutic agent and / or radiology), other treatments can be planned and used without wasting valuable time. Can be.

  Modern molecular biology and biochemistry, with some exceptions, have revealed hundreds of genes whose activity affects tumor cell behavior and differentiation status and sensitivity or resistance to specific therapeutics. However, the status of these genes has not been developed for the purpose of routine clinical decisions about drug treatment. One notable exception is the use of estrogen receptor (ER) protein expression in breast cancer to select patients to be treated with antiestrogens (eg, tamoxifen). Another exceptional example is the use of ErbB2 (Her2) protein expression in breast cancer to select patients to be treated with the anti-Her2 antibody Herceptin® (Genentech, Inc., South San Francisco, Calif.). It is.

The present invention relates to cancers whose biological understanding has not yet progressed (eg breast cancer). Classification of breast cancer into a small subgroup, such as the ErbB2 ⁺ subgroup, and subgroups characterized by low or lack of gene expression of estrogen receptor (ER) and a few additional factors (Perou et al., Nature 406: 747-752 (2000)) does not reflect the cellular and molecular diversity of breast cancer and does not allow patient treatment and medical optimization. The same is true for other types of cancer, and many of these cancers have been studied and understood much less than breast cancer.

  Currently, diagnostic tests used in clinical practice are single analytes and thus fail to capture the potential significance of knowing the association between dozens of different tumor markers. Furthermore, diagnostic tests rely on immunohistochemistry and are often not quantitative. This method often gives different results in different laboratories. This is because, in part, these reagents are not standardized, and in part, the interpretation is subjective and cannot be easily quantified. RNA-based tests are not used very often. Because of the problem of RNA degradation over time and the fact that it is difficult to obtain fresh tissue samples for analysis from patients. Fixed paraffin embedded tissues are more accessible and methods have been established to detect RNA in fixed tissues. However, these methods typically do not allow the study of large numbers of genes derived from small amounts of material. Thus, traditionally, fixed tissues have been rarely used except for immunohistochemical detection of proteins.

In the past few years, several groups have published work on the classification of various cancer types by microarray gene expression analysis {see, eg, Golub et al. , Science 286: 531-537 (1999); Bhattacharjae et al. , Proc. Natl. Acad. Sci. USA 98: 13790-13795 (2001); Chen-Hsiang et al. Bioinformatics 17 (Appendix 1): S316-S322 (2001); Ramawamy et al. , Proc. Natl. Acad. Sci. USA 98: 15149-15154 (2001)}. Certain classifications of human breast cancer based on gene expression patterns have also been reported {Martin et al. , Cancer Res. 60: 2232-2238 (2000); West et al. , Proc. Natl. Acad. Sci. USA 98: 11462-11467 (2001)}. Most of these studies focus on improving and refining the already established classification of various types of cancer (including breast cancer). A few studies have identified gene expression patterns that may be prognostic signs {Sorlie et al. , Proc. Natl. Acad. Sci. USA 98: 10869-10874 (2001); Yan et al. , Cancer Res. 61: 8375-8380 (2001); Van De Vivjer et al. , New England Journal of Medicine 347: 1999-2009 (2002)}, because of the insufficient number of patients screened, it is not effective enough to be widely used clinically.
Perou et al. , Nature 406: 747-752 (2000). Golub et al. , Science 286: 531-537 (1999); Bhattacharjae et al. , Proc. Natl. Acad. Sci. USA 98: 13790-13795 (2001); Chen-Hsiang et al. , Bioinformatics 17 (Appendix 1): S316-S322 (2001) Ramawamy et al. , Proc. Natl. Acad. Sci. USA 98: 15149-15154 (2001) Martin et al. , Cancer Res. 60: 2232-2238 (2000); West et al. , Proc. Natl. Acad. Sci. USA 98: 11462-11467 (2001) Sollie et al. , Proc. Natl. Acad. Sci. USA 98: 10869-10874 (2001) Yan et al. , Cancer Res. 61: 8375-8380 (2001) Van De Vivjer et al. , New England Journal of Medicine 347: 1999-2009 (2002).

  The present invention provides an algorithm-based prognostic test to determine the likelihood of cancer recurrence and / or the likelihood that a patient will respond well to therapy. In certain aspects, the present invention provides algorithms and prognostic tests to determine the likelihood of patient response to breast cancer recurrence and / or treatment in patients with invasive breast cancer. Thus, the present invention finds utility in treatment decisions relating to the treatment of cancer patients such as, for example, lymph node negative breast cancer patients who have undergone surgical resection of the tumor. Specifically, this algorithm and associated prognostic tests help determine whether such patients are treated with adjuvant chemotherapy, and if the answer is positive, the treatment option is selected. The

  Features that distinguish this algorithm from other breast cancer prediction methods include the following 1) -3): 1) Uniqueness of test mRNA (or corresponding gene expression product) used to determine recurrence probability 2) specific weights used to incorporate expression data into the formula, 3) classifying patients into groups with different levels of risk (eg, low risk group, medium risk group, and high risk group) Threshold used for. This algorithm obtains a numerical recurrence score (RS) or, if patient response to treatment is evaluated, a response to therapy score (RTS). This test requires a laboratory assay to measure the level of specific mRNAs or their expression products, but it is inevitably collected and stored from a very small amount of fresh tissue or already from the patient. Either frozen tissue or fixed paraffin-embedded tumor biopsy specimens can be used. Therefore, this test can be non-invasive. This test may also be compatible with several different tumor tissue collection methods (eg, by core biopsy or fine needle aspiration). This tumor tissue can be removed with the naked eye from normal tissue, but it need not be. Furthermore, for each member of the gene set, the present invention identifies an oligonucleotide sequence that can be used in this test. These mRNA levels are normalized to a specific set of reference genes. The present invention encompasses all subsequences of test and reference genes in a similar manner.

Accordingly, in one aspect, the invention relates to a method of classifying tumors based on the likelihood of cancer recurrence or response to treatment in a mammalian subject, the method comprising the following steps:
(A) subjecting a biological sample containing cancer cells obtained from the subject to gene or protein expression profiling;
(B) quantifying the gene or protein expression level of a plurality of individual genes to determine the expression value for each gene;
(C) creating subsets of gene or protein expression values, each subset comprising expression values for genes or proteins that are linked by biological functions associated with cancer and / or by co-expression Process;
(D) multiplying the expression level of each gene in the subset by a factor that reflects its relative contribution to the recurrence of cancer or response to treatment in the subset, and adding this product to the term for the subset Obtaining step;
(E) multiplying each subset term by a conversion factor that reflects its contribution to cancer recurrence or response to treatment; and (f) determining the sum of the terms for each subset multiplied by the conversion factor. Obtaining a recurrence score (RS) or a response score to therapy (RTS),
Here, the contribution of each subset that does not show a linear correlation with cancer recurrence or response to treatment is included only if it exceeds a pre-determined threshold, and where the expression of the included gene or protein increases the cancer A subset with a reduced risk of recurrence is assigned a negative value, and a subset with an increased expression of an included gene or protein increases the risk of cancer recurrence is assigned a positive value.

  This algorithm is suitable for assessing the risk of cancer recurrence or response to therapy for any type of cancer, including but not limited to breast cancer.

  In the case of breast cancer, the genes contributing to this recurrence algorithm include 16 test genes and 5 reference genes.

  The designated test genes / mRNA are: Grb7; Her2; ER; PR; BCl2; CEGP1; SURV; Ki-67; MYBL2; CCNB1; STK15; CTSL2; STMY3;

  Reference genes / mRNAs are: β-ACTIN; GAPDH; RPLPO; TFRC; GUS.

  For breast cancer, the set of 16 test genes above includes 4 multigene subsets. The gene members of these subsets have common features in both correlated expression and the following categories of biological functions: growth subsets including SURV, Ki-67, MYBL2, CCNB1 and STK15; growth factors including Her2 and Grb7 Subset; Estrogen receptor subset including ER, PR, BCl2, and CEGP1; Invasive subset including genes for the invasive proteases CTSL2 and STMY3.

Accordingly, in a particular aspect, the present invention relates to a method for determining the likelihood of breast cancer recurrence or response to hormonal therapy in a mammalian subject, said method comprising the following steps:
(A) In a biological sample containing tumor cells obtained from the subject, GRB7, HER2, ER, PR, Bcl2, CEGP1, SURV, Ki. Determining the expression level of 67, MYBL2, CCNB1, STK15, CTSL2, and STMY3 RNA transcripts, or their expression products, or corresponding alternative genes or their expression products;
(B) The following gene subset:
(I) Growth factor subset: GRB7 and HER2;
(Ii) Differentiation (estrogen receptor) subset: ER, PR, Bcl2, and CEGP1;
(Iii) Proliferation subset: SURV, Ki. 67, MYBL2, CCNB1, and STK15; and (iv) Invasive subsets: CTSL2, and STMY3;
The process of producing
Wherein genes in any subset of (i)-(iv) can be replaced with alternative genes that are co-expressed with the gene in the tumor having a Pearson correlation coefficient of 0.40 or greater; And (c) a recurrence score (RS) or response to treatment score (RTS) for the subject by weighting the contribution of each subset of (i)-(iv) to the recurrence of breast cancer or response to treatment. ).

In certain embodiments, the method comprises determining the expression levels of CD68, GSTM1 and BAG1 RNA transcripts or their expression products, or corresponding alternative genes or their expression products, and breast cancer recurrence or treatment. Further comprising the step of incorporating into the RS or RTS calculation the contribution of said gene or surrogate gene to the response of
Here, a higher RS or RTS represents an increased likelihood of breast cancer recurrence or a reduced response to treatment (if applicable).

In certain aspects, the invention relates to a method for determining the likelihood of recurrence of breast cancer in a mammalian subject, the method comprising the following steps:
(A) In a biological sample containing tumor cells obtained from the subject, GRB7, HER2, EstRl, PR, Bcl2, CEGP1, SURV, Ki. 67, determining the expression level of MYBL2, CCNB1, STK15, CTSL2, STMY3, CD68, GSTM1, and BAG1, or their expression products; and (b) the following equation:
RS = (0.23-0.70) × GRB7-axis threshold (axisthresh) − (0.17-0.51) × ER-axis + (0.53-1.56) × proliferation-axis threshold (prolifaxithresh) + ( 0.07 to 0.21) × invasive axis + (0.03 to 0.15) × CD68− (0.04 to 0.25) × GSTM1− (0.05 to 0.22) × BAG1
Calculating a recurrence score (RS) by:
here,
(I) GRB7 axis = (0.45-1.35) × GRB7 + (0.05-0.15) × HER2;
(Ii) If GRB7 axis <-2, then GRB7 axis threshold = -2, and
If GRB7 axis ≧ −2, GRB7 axis threshold = GRB7 axis;
(Iii) ER axis = (Est1 + PR + Bcl2 + CEGP1) / 4;
(Iv) Growth axis = (SURV + Ki.67 + MYBL2 + CCNB1 + STK15) / 5;
(V) if growth axis <−3.5, growth axis threshold = −3.5;
If growth axis ≧ −3.5, growth axis threshold = proliferation axis; and (vi) invasive axis = (CTSL2 + STMY3) / 2,
Here, the terms for all individual genes whose ranges are not specifically indicated can vary between about 0.5 and 1.5, and where higher RS is the likelihood of breast cancer recurrence A process that represents an increase in sex.

Laboratory assays for gene expression can use a variety of different platforms. A preferred embodiment for use with fixed paraffin embedded tissue is quantitative reverse transcriptase polymerase chain reaction (qRT-PCR). However, other technology platforms including mass spectrometry and DNA microarrays can also be used. For DNA microarrays, the polynucleotide probes can typically be cDNAs of about 500 to 5000 bases in length (“cDNA arrays”), although shorter or longer cDNAs can be used and are within the scope of the present invention. Is within. Alternatively, these polynucleotides can be oligonucleotides (DNA microarrays), typically about 20-80 bases in length, although shorter or longer oligonucleotides are also suitable, and these are Within range. This solid surface can be, for example, glass or nylon, or any other solid surface typically used in the preparation of arrays (eg, microarrays), typically glass.
Detailed Description of Preferred Embodiments
A. Definitions Unless defined otherwise, technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention belongs. Singleton et al. , Dictionary of Microbiology and Molecular Biology 2nd ed. , J. et al. Wiley & Sons (New York, NY 1994), and March, Advanced Organic Chemistry Reactions, Mechanisms and Structure 4th ed. John Wiley & Sons (New York, NY 1992) provides general guidance to those skilled in the art for a number of terms used in this application.

  Those skilled in the art will recognize many methods and materials similar or equivalent to those described herein, which can be used in the practice of the present invention. Indeed, the present invention is in no way limited to the methods and materials described. For purposes of the present invention, the following terms are defined below.

  The term “microarray” refers to an ordered arrangement of hybridizable array elements, preferably polynucleotide probes, on a substrate.

  The term “polynucleotide”, when used in the singular or plural, generally refers to any polyribonucleotide or polydeoxyribonucleotide, including unmodified RNA or DNA, modified RNA or DNA. May be. Thus, for example, polynucleotides as defined herein include, but are not limited to, single stranded and double stranded DNA, single stranded and double stranded DNA, single stranded and double stranded. Including single-stranded RNA, and RNA containing single-stranded and double-stranded regions, DNA and RNA that can be single-stranded or more typically double-stranded or contain single-stranded and double-stranded regions Hybrid molecules are mentioned. Further, as used herein, the term “polynucleotide” refers to triple-stranded regions comprising RNA or DNA or both RNA and DNA. The chains in such regions can be derived from the same molecule or from different molecules. These regions may include all of one or more molecules, but more typically include only one region of some of these molecules. One of these triple helix region molecules is often an oligonucleotide. The term “polynucleotide” specifically includes cDNA. The term includes DNA (including cDNA) and RNA containing one or more modified bases. Thus, a DNA or RNA having a backbone modified for stability or for other reasons is a “polynucleotide” as that term is intended herein. In addition, DNA or RNA containing unusual bases (eg, inosine) or modified bases (eg, tritiated bases) are included within the term “polynucleotide” as defined herein. In general, the term “polynucleotide” refers to all chemically, enzymatically and / or metabolically modified forms of unmodified polynucleotides, and cells and viruses including simple and complex cells. Including the chemical forms of DNA and RNA unique to

  The term “oligonucleotide” refers to a relatively short polynucleotide, including but not limited to single-stranded deoxyribonucleotides, single- or double-stranded ribonucleotides, RNA: DNA hybrids and double-stranded DNA. Oligonucleotides (eg, single-stranded DNA probe oligonucleotides) are often synthesized by chemical methods (eg, using commercially available automated oligonucleotide synthesizers). However, oligonucleotides can be made by a variety of other methods, including in vitro recombinant DNA-mediated techniques, and by expression of DNA in cells and organisms.

  The terms “differentially expressed gene”, “differential gene expression” and their synonyms, used interchangeably, refer to a subject afflicted with a disease (specifically, cancer, such as breast cancer). A gene whose expression is activated to a higher or lower level as compared to expression in normal or control subjects. These terms also encompass genes whose expression is activated to higher or lower levels at different stages of the same disease. It is also understood that differentially expressed genes may be activated or inhibited at the nucleic acid level or protein level, or may produce different polypeptide products as a result of undergoing alternative splicing. Such differences can be evidenced, for example, by changes in mRNA levels, surface expression, secretion or other polypeptide compartments. Differential gene expression is a comparison of expression between two or more genes or their gene products, or a comparison of expression ratios between two or more genes or their gene products, or two different process products of the same gene May also be included, which differ between normal subjects and subjects with a disease (specifically cancer) or between various stages of the same disease. Differential expression is, for example, quantitative differences and quality in the transient or cellular expression pattern of a gene or its expression product between normal and abnormal cells, or between cells that have experienced different disease events or stages. Including both differences. For purposes of the present invention, at least about 2-fold, preferably at least about 4-fold, and more during expression of a given gene in normal and disease subjects, or at various disease progression stages of a disease subject, “Differential gene expression” is considered to be present if there is a difference of preferably at least about 6 fold, most preferably at least about 10 fold.

  The phrase “gene amplification” refers to the process by which multiple copies of a gene or gene fragment are formed in a particular cell or cell line. This replication region (extension of amplified DNA) is often referred to as an “amplicon”. Usually, the amount of messenger RNA (mRNA) produced (ie, the level of gene expression) also increases in proportion to the number of copies made of the particular gene expressed.

  The term “prognosis” is used herein to refer to a prediction of progression including death from cancer or neoplastic disease (eg, breast cancer), metastatic spread, and drug resistance. Is done.

  The term “prediction” refers to the likelihood that a patient will respond favorably or unfavorably to a drug or series of drugs, and also the extent of those responses, or the surgical excision and / or chemotherapy of the primary tumor. Is used herein to refer to the possibility of surviving for a period of time without recurrence of cancer. The prediction method of the present invention is whether a patient is likely to respond advantageously to a treatment regimen (eg, surgical treatment, chemotherapy with a given drug or combination of drugs, and / or radiation therapy). Or a useful tool for predicting whether a patient's long-term survival is promising after surgery and / or after completion of chemotherapy or other treatment.

  The term “long term” survival is used herein to refer to survival for at least 5 years, more preferably at least 8 years, most preferably at least 10 years after surgery or other procedures.

  The term “tumor” as used herein refers to the growth and proliferation of all neoplastic cells, whether malignant or benign, and all precancerous and cancerous cells and tissues.

  The terms “cancer” and “cancerous” refer to or describe the physiological condition in mammals that is typically characterized by unregulated cell growth. Examples of cancer include breast cancer, colon cancer, lung cancer, prostate cancer, hepatocellular carcinoma, gastric cancer, pancreatic cancer, cervical cancer, ovarian cancer, liver cancer, bladder cancer, urinary tract cancer, thyroid cancer, kidney cancer, carcinoma, black Tumors, and brain tumors, but are not limited to these.

  The “pathology” of (tumor) cancer encompasses all phenomena that impair the health of the patient. This includes, but is not limited to, abnormal or uncontrollable cell growth, metastasis, interference with normal functioning of surrounding cells, release of cytokines or other secretions at abnormal levels, suppression or exacerbation of inflammatory or immune responses , Neoplasia, pre-malignant tumor, malignant tumor, invasion of surrounding or distant tissues or organs (eg lymph nodes) and the like.

  The “stringency” of a hybridization reaction is readily determinable by one of ordinary skill in the art and is generally an empirical calculation that depends on probe length, wash temperature, and salt concentration. In general, longer probes require higher temperatures for proper annealing, while shorter probes require lower temperatures. Hybridization generally depends on the ability of denatured DNA to reanneal when the complementary strand is present in an environment below these melting points. The higher the degree of desired homology between the probe and hybridizable sequence, the higher the relative temperature that can be used. As a result, higher relative temperatures make the reaction conditions more stringent, while lower temperatures tend to make the reaction conditions less stringent. For further details and explanation of the stringency of hybridization reactions, see Ausubel et al. , Current Protocols in Molecular Biology, Wiley Interscience Publishers, (1995).

  As defined herein, “stringent conditions” or “high stringent conditions” are typically: (1) using low ionic strength and high temperature for washing, eg, Use 0.015 M sodium chloride / 0.0015 M sodium citrate / 0.1% sodium dodecyl sulfate at 50 ° C .; (2) Use denaturing agents (eg, formamide) during hybridization, eg, 42 ° C. 50% containing 50 mM sodium phosphate buffer (pH 6.5) containing 0.1% bovine serum albumin / 0.1% Ficoll / 0.1% polyvinylpyrrolidone / 750 mM sodium chloride, 75 mM sodium citrate. v / v) use formamide; or (3) 50% formamide at 42 ° C., 5 × SSC (0.7 M NaCl, 0.075 M sodium citrate), 50 mM sodium phosphate (pH 6.8), 0.1% sodium pyrophosphate, 5 × Denhardt solution, sonicated salmon sperm DNA (50 μg / mL), 0.1 From 0.2 × SSC (sodium chloride / sodium citrate) at 42 ° C., 50% formamide at 55 ° C., followed by 0.1 × SSC containing EDTA at 55 ° C. using 10% SDS, and 10% dextran sulfate Wash with a high stringency wash solution that is composed.

  “Moderately stringent conditions” are described in Sambrook et al. , Molecular Cloning: A Laboratory Manual, New York: Cold Spring Harbor Press, 1989, and washing conditions and hybridization conditions that are less stringent than those described above (eg, temperature, ion Use of strength and SDS%). Examples of moderately stringent conditions include solutions (including: 20% formamide, 5 × SSC (150 mM NaCl, 15 mM trisodium citrate), 50 mM sodium phosphate (pH 7.6), 5 × Denhardt solution, Incubate overnight at 37 ° C. in 10% dextran sulfate, and 20 mg / mL denatured cut salmon sperm DNA, followed by washing the filter in 1 × SSC at approximately 37-50 ° C. Those skilled in the art know how to adjust the temperature, ionic strength, etc. necessary to accommodate factors such as probe length.

  In the context of the present invention, “at least one”, “at least two”, “at least five”, etc. of the genes listed in any particular gene set are any of the genes listed Mean one or any combination.

  The term “(lymph) node negative” cancer, eg, “(lymph) node negative” breast cancer, is used herein to refer to a cancer that has not spread to the lymph nodes.

  The term “gene expression profiling” is used in the broadest sense and includes methods for quantifying mRNA and / or protein levels in a biological sample.

  The term “adjuvant therapy” is generally used for treatments offered in addition to primary (initial) therapy. In cancer treatment, the term “adjuvant therapy” is used to refer to chemotherapy, hormonal therapy and / or radiation therapy after surgical excision of a tumor, whose primary purpose is to reduce the risk of cancer recurrence.

  “Neoadjuvant therapy” is an adjuvant or adjuvant therapy that is given before the primary (primary) therapy. Neoadjuvant therapy includes, for example, chemotherapy, radiation therapy, and hormone therapy. Thus, chemotherapy can be performed before surgery to shrink the tumor, so that surgery can be more effective, or surgery can be possible in the case of tumors that are already inoperable .

  The term “biological function associated with cancer” refers to molecular activities that affect cancer success against the host (including but not limited to cell proliferation, programmed cell death (apoptosis), differentiation, invasion, Metastasis, tumor suppression, susceptibility to immune surveillance, angiogenesis, activity that modulates the maintenance or acquisition of immortality) is used herein to refer to.

B. DETAILED DESCRIPTION Unless otherwise indicated, the practice of the present invention uses conventional techniques of molecular biology (including recombinant technology), microbiology, cell biology, and biochemistry, which are well known in the art. Within range. Such techniques are described, for example, in “Molecular Cloning: A Laboratory Manual”, 2nd edition (Sambrook et al., 1989); “Oligonucleotide Synthesis” (M. J. Gait, ed., 1984); R. I. Freshney, ed., 1987); “Methods in Enzymology” (Academic Press, Inc.); “Handbook of Experimental Immunology, 4th edition, C. Wel. C & W. Inc., 1987); “Gene Transfer Vectors. “For Mammalian Cells” (JM Miller & MP Calos, ed., 1987); “Current Protocols in Molecular Biology” (F. M. Ausubel et al., ed., 1987); and “PCR: The Polymerase C ”, (Mullis et al., Ed., 1994).

  The present invention provides algorithms for determining the likelihood of cancer recurrence or response to treatment in cancer patients. This method is based on the following (1)-(3): (1) Identification of a gene set that can function as a marker of cancer recurrence and clustering or the possibility of patient response to a specific treatment, (2) Cluster and Specific weights assigned to individual genes in the cluster (which reflect their values in predicting cancer recurrence or response to treatment) and used to incorporate expression data into the formula; and (3 ) Groups with varying degrees of cancer recurrence, or with varying likelihood of response to treatment (eg, low-risk, moderate-risk, and high-risk groups, or patient response to specific treatment) Determination of thresholds used to classify patients into low sex, moderate or high groups. This algorithm provides a numerical recurrence score (RS) or response to treatment score (RTS), which can be used to make treatment decisions regarding the treatment of cancer patients.

  The first step in generating data to be analyzed by the algorithm of the present invention is gene or protein expression profiling.

1. Expression Profiling Techniques The present invention requires an assay to measure the level of specific genes (mRNA) or their expression products (proteins) in a biological sample containing cancer cells. Typically, this biological sample is a fresh or stored tissue sample obtained from a tumor, for example, by tumor biopsy or aspiration, but biological fluids containing tumor cells can also be used for analysis. .

  In the most common form, gene expression profiling involves the determination of mRNA levels in a tissue sample such as, for example, a fixed paraffin-embedded tumor biopsy specimen that has already been collected and stored from a patient. Therefore, this test can be completely non-invasive. This test may also be compatible with several different tumor tissue collection methods (eg, core biopsy or fine needle aspiration). This tumor tissue can be removed with the naked eye from normal tissue, but it need not be.

  Methods of gene expression profiling directed to the measurement of mRNA levels can be divided into two large groups: methods based on polynucleotide hybridization analysis, and methods based on polynucleotide sequencing. The methods most commonly used in the art for quantifying mRNA expression in a sample include Northern blotting and in situ hybridization (Parker & Barnes, Methods in Molecular Biology 106: 247-283 (1999)); RNAse protection assay (Hod) , Biotechniques 13: 852-854 (1992)); and reverse transcription polymerase chain reaction (RT-PCR) (Weis et al., Trends in Genetics 8: 263-264 (1992)). Alternatively, antibodies that can recognize specific duplexes, including DNA duplexes, RNA duplexes, and DNA-RNA hybrid duplexes or DNA-protein duplexes, can be used. Representative methods for gene expression analysis based on sequencing include gene expression analysis by continuous gene expression analysis (SAGE) and massively parallel gene bead clone analysis (MPSS).

  In all of these techniques, the first step is the isolation of mRNA from the target sample. This starting material is typically total RNA isolated from human tumors or tumor cell lines, and corresponding normal tissues or cell lines, respectively. Thus, RNA uses pool DNA from healthy donors to produce a variety of primary tumors including tumors such as breast, lung, colon, prostate, brain, liver, kidney, pancreas, spleen, thymus, testis, ovary, uterus, etc. Or isolated from a tumor cell line. If the source of the mRNA is a primary tumor, the mRNA can be extracted from, for example, frozen or stored paraffin embedded and fixed (eg, formalin fixed) tissue samples.

The general method of mRNA extraction is a well-known technique and is described in Ausubel et al. , Current Protocols of Molecular Biology, John Wiley and Sons (1997). Methods for RNA extraction from paraffin-embedded tissues are described, for example, in Rupp and Locker, Lab Invest. 56: A67 (1987), and De Andres et al. BioTechniques 18: 42044 (1995). In particular, RNA isolation can be performed using purification kits, buffer sets and proteases provided by commercial manufacturers (eg, Qiagen) according to the manufacturer's instructions. For example, total RNA from cultured cells can be isolated using a Qiagen RNeasy mini column. Other commercially available RNA isolation kits include MasterPure ^™ complete DNA and RNA purification kits (EPICENTRE®, Madison, Wis.), And paraffin block RNA isolation kits (Ambion, Inc.). ). Total RNA from tissue samples can be isolated using RNA Stat-60 (Tel-Test). RNA prepared from a tumor can be isolated, for example, by cesium chloride density gradient centrifugation.

  Although the practice of the present invention will be described with reference to techniques developed to determine mRNA levels in biological (eg tissue) samples, other techniques such as methods of proteomic analysis are also broad. Included in conceptual gene expression profiling and within the scope of this specification. In general, the preferred gene expression profiling method using paraffin-embedded tissue is quantitative reverse transcriptase polymerase chain reaction (qRT-PCR), but other technology platforms can be used, including mass spectrometry and DNA microarrays.

  In the following sections, a representative but not exhaustive series of gene expression profiling techniques is discussed in more detail.

2. Reverse transcriptase PCT (RT-PCR)
Of the above techniques, the most sensitive and most adaptable quantification method is qRT-PCR, which is the mRNA level of different sample populations in normal and tumor tissues with or without drug treatment. Can be used to characterize patterns of gene expression, distinguish between closely related mRNAs, and analyze RNA structure.

  Since RNA cannot be a template for PCR, the first step in gene expression profiling by RT-PCR is reverse transcription of the RNA template into cDNA followed by exponential amplification in the PCR reaction. The two most commonly used reverse transcriptases are avian myeloblastosis virus reverse transcriptase (AMV-RT) and Moloney murine leukemia virus reverse transcriptase (MMLV-RT). The reverse transcription step is typically primed with specific primers, random hexamers, or oligo-dT primers, depending on the status and purpose of expression profiling. For example, extracted RNA can be reverse transcribed using a GeneAmp RNA PCR kit (Perkin Elmer, CA, USA) according to the manufacturer's instructions. The generated cDNA can then be used as a template for subsequent PCR reactions.

  The PCR process can use various thermostable DNA-dependent DNA polymerases, but typically uses Taq DNA polymerase, which has 5′-3 ′ nuclease activity but a 3′-5 ′ proofreading end. It lacks nuclease activity. Thus, TaqMan® PCR typically utilizes the 5′-nuclease activity of Taq or Tth polymerase to hydrolyze the hybridization probe bound to the target amplicon, but the equivalent 5 ′ nuclease activity. Any enzyme having can be used. Two oligonucleotide primers are used to generate an amplicon typical of a PCR reaction. A third oligonucleotide (ie probe) is designed to detect the nucleotide sequence located between the two PCR primers. This probe cannot be extended by Taq DNA polymerase enzyme and is labeled with a reporter fluorescent dye and a quencher fluorescent dye. Any laser-induced emission from the reporter dye is quenched by the quenching dye when located in close proximity to each other such that these two dyes are on the probe. During this amplification reaction, the Taq DNA polymerase enzyme cleaves the probe in a template-dependent manner. The resulting probe fragment is separated into solution and the signal from the released reporter dye is not subject to the quenching action of the second fluorophore. One molecule of reporter dye is released for each new molecule synthesized and the basis for quantitative interpretation of this data is obtained by detecting the unquenched reporter dye.

TaqMan® RT-PCR can be performed, for example, by ABI PRISM 7700 ^™ Sequence Detection System ^™ (Perkin-Elmer-Applied Biosystems, Foster City, Calif., USA) or Lightcycler (Roche Molecular Biochemistry, Roche Molecular Biochem. It can be carried out using commercially available equipment. In a preferred embodiment, the 5 'nuclease procedure is performed on a real-time quantitative PCR device such as the ABI PRISM 7700 ^TM sequence detection system ^TM . The system consists of a thermocycler, laser, charge coupled device (CCD), camera and computer. The system includes software for operating the instrument and analyzing the data.

  5'-nuclease assay data is initially expressed as Ct (ie, threshold cycle). As described above, fluorescence values are recorded every cycle and represent the amount of product amplified to that point in the amplification reaction. The point at which the fluorescent signal is first recorded as statistically significant is the threshold cycle (Ct).

  To minimize the effects of errors and sample-to-sample variation, RT-PCR is usually performed using an internal standard. The ideal internal standard is expressed at a constant level between different tissues and is not affected by experimental treatment. The RNAs most frequently used to normalize gene expression patterns are the housekeeping gene glyceraldehyde 3-phosphate dehydrogenase (GAPDH) and β-actin mRNA.

  A recent variation of RT-PCR technology is real-time quantitative PCR, which measures PCR product accumulation with a dual-labeled fluorescent probe (ie, TaqMan® probe). Real-time PCR is compatible with both quantitative competitive PCR using internal competitors for each target sequence for normalization, and quantitative comparative PCR using normalized or housekeeping genes contained in the sample for RT-PCR. For further details, see, eg, Held et al., Genome Research 6: 986-994 (1996).

3. Microarray Differential gene expression can also be identified or confirmed using microarray technology. Thus, the expression profile of breast cancer-related genes can be measured in either fresh tumor tissue or paraffin-embedded tumor tissue using microarray technology. In this method, the polynucleotide sequence of interest (including cDNA and oligonucleotides) is plated or arrayed on a microchip substrate. These arrayed sequences are then hybridized with specific DNA probes from the cell or tissue of interest. Similar to the RT-PCR method, the source of mRNA is typically total RNA isolated from human tumors or tumor cell lines, and corresponding normal tissues or cell lines. Thus, RNA can be isolated from a variety of primary tumors or tumor cell lines. If the source of the mRNA is the primary tumor, then the mRNA is, for example, frozen or stored in paraffin-embedded and fixed (eg formalin-fixed) tissue samples (these are routinely used in daily clinical practice. Prepared and stored).

  In a specific embodiment of microarray technology, PCR amplified cDNA clone inserts are applied to a substrate in a dense array. Preferably, at least 10,000 nucleotide sequences are applied to the substrate. Microarrayed genes immobilized at 10,000 elements each on a microchip are suitable for hybridization under stringent conditions. Fluorescently labeled cDNA probes can be generated by incorporation of fluorescent nucleotides by reverse transcription of RNA extracted from the tissue of interest. Labeled cDNA probes applied to the chip are hybridized with specificity for each spot of DNA on the array. After stringent washing to remove non-specifically bound probes, the chip is scanned with a confocal laser microscope or with another detection method (eg, a CCD camera). Quantification of the hybridization of each arraying element allows the assessment of the corresponding mRNA abundance. CDNA probes generated from two RNA sources and separately labeled with two-color fluorescence are hybridized to the array in duplicate. Thus, the relative abundance of transcripts from the two sources corresponding to each particular gene is determined simultaneously. By reducing the scale of hybridization, a simple and rapid evaluation of expression patterns for a large number of genes can be obtained. Such a method should have the sensitivity necessary to detect rare transcripts (expressed at several copies per cell) and to reproducibly detect at least about a 2-fold difference in expression levels. (Schena et al., Proc. Natl. Acad. Sci. USA 93 (2): 106-149 (1996)). Microarray analysis can be performed by commercially available equipment (eg, by using Affymetrix GenChip technology, or Incyte's microarray technology) according to the manufacturer's protocol.

  Development of microarray methods for large-scale analysis of gene expression enables systematic searches for molecular markers for cancer classification and prognosis prediction in various tumor types.

4). Continuous gene expression analysis (SAGE)
Sequential gene expression analysis (SAGE) is a method that allows simultaneous quantitative analysis of multiple gene transcripts without the need to provide individual hybridization probes for each transcript. First, a short sequence tag (about 10-14 base pairs) containing sufficient information to uniquely identify a transcript is generated, provided that the tag is obtained from a unique position in each transcript. Multiple transcripts are then ligated to form long continuous molecules that can be sequenced, while simultaneously revealing the identity of multiple tags. The expression pattern of any population of transcripts can be assessed quantitatively by determining the abundance of individual tags and identifying the genes corresponding to each tag. For further details, see, for example, Velculescu et al. , Science 270: 484-487 (1995); and Velculescu et al. , Cell 88: 243-51 (1997).

5. Gene expression analysis by massively parallel gene bead clone analysis (MPSS) This method is described in Brenner et al. , Nature Biotechnology 18: 630-634 (2000), a sequencing approach that combines non-gel based signature sequencing and in vitro cloning of millions of templates on individual 5 μm diameter microbeads. First, a DNA template microbead library is constructed by in vitro cloning. Subsequently, a planar array of template-containing microbeads in the flow cell is assembled at high density (typically at a density higher than 3 × 10 ⁶ microbeads / cm ² ). The free ends of the cloned template on each microbead are analyzed simultaneously using a fluorescence-based signature sequencing method that does not require DNA fragment separation. This method has been shown to produce hundreds of thousands of gene signature sequences simultaneously and accurately from a yeast cDNA library in a single operation.

6). Immunohistochemistry Immunohistochemistry methods are also suitable for detecting the expression levels of the prognostic markers of the present invention. Thus, antibodies or antisera, preferably polyclonal antisera, and most preferably monoclonal antibodies specific for each marker are used to detect expression. These antibodies can be detected, for example, by direct labeling of the antibody itself, using radioactive labels, fluorescent labels, hapten labels such as biotin, or enzymes such as horseradish peroxidase or alkaline phosphatase. Alternatively, an unlabeled primary antibody is used in conjunction with a labeled secondary antibody that includes an antiserum, polyclonal antiserum or monoclonal antibody specific for the primary antibody. Immunohistochemistry protocols and kits are well known in the art and are commercially available.

7). Proteomics The term “proteome” is defined as the entire protein present in a sample (eg, tissue, organism, or cell culture) at a particular time. Among other things, proteomics encompasses studies of the overall change in protein expression in a sample (also referred to as “expression proteomics”). Proteomics typically includes the following steps: (1) Separation of individual proteins in a sample by two-dimensional gel electrophoresis (2-D PAGE); (2) by, for example, mass spectrometry or N-terminal sequencing , Identification of individual proteins recovered from the gel, and (3) analysis of data using bioinformatics. Proteomic methods are a valuable supplement to other methods of gene expression profiling and can be used alone or in combination with other methods to detect the production of prognostic markers of the present invention.

8). Clinical application of oncogene sets, assayed gene sequences and gene expression data An important aspect of the present invention is to measure the expression of specific genes (or their expression products) by cancer (eg breast cancer) tissue and obtain prognostic information To use. This requires correction (normalization) for both differences in the amount of RNA assayed and variability in the quality of RNA used. Thus, this assay typically measures and incorporates the expression of specific normalized genes, including well-known housekeeping genes (eg, GAPDH and β-ACTIN). Alternatively, normalization can be based on the average or median signal (Ct) of all assayed genes or their large subunits (overall normalization approach). Throughout the disclosure, unless otherwise stated, references to gene expression levels are considered not normalized expression but normalized expression relative to a reference set.

9. Algorithm for obtaining a cancer recurrence score When measuring mRNA levels using qRT-PCR, the amount of mRNA is expressed in units of Ct (threshold cycle) (Held et al., Genome Research 6: 986-994 (1996)). . The average of the total reference mRNA Ct was set to zero and each measured test mRNA Ct was obtained with reference to this zero point. For example, for a particular patient tumor specimen, if the average Ct of 5 reference genes is 31 and the Ct of the test gene CRB7 is determined to be 35, the reported value for GRB7 is -4 (ie 31-35 ).

  After quantifying mRNA levels, as a first step, genes identified in tumor specimens and genes known to be associated with the molecular pathology of cancer are grouped into subsets. Thus, genes known to be associated with proliferation constitute a “growth subset” (axis). Genes known to be associated with cancer invasion constitute an “invasive subset” (axis). Genes associated with major growth factor receptor pathways constitute a “growth factor subset” (axis). Genes known to be involved in activation or signaling through the estrogen receptor (ER) constitute the “estrogen receptor subset” (axis). This list of subsets is of course not limiting. These created subsets (axes) depend on the specific cancer (ie, breast cancer, prostate cancer, pancreatic cancer, lung cancer, etc.). Usually, genes whose expression is known to correlate with each other or are known to be involved in the same pathway are grouped on the same axis.

  In the next step, the measured tumor level of each mRNA in the subset is multiplied by a factor that reflects the relative intraset contribution to the risk of cancer recurrence, and this product is multiplied by the mRNA level in the subset and those factors. Add to other products in between to get terms (eg, growth terms, invasive terms, growth factor terms, etc.). For example, in the case of lymph node negative invasive breast cancer, the growth factor term is (0.45-1.35) × GRB7 + (0.05-0.15) × Her2 (see examples below).

  The contribution rate of each term to the overall recurrence score is weighted using a coefficient. For example, in the case of lymph node negative invasive breast cancer, the coefficient of the growth factor term can be between 0.23 and 0.70.

  Furthermore, additional steps are performed for some terms (eg, growth factor terms and proliferation terms). If the relationship between the term and the risk of recurrence is non-linear, a non-linear function transformation (eg, threshold) for this term is used. Thus, in lymph node negative invasive breast cancer, this value is fixed at -2 if the growth factor term is less than -2. If the growth term is less than -3.5, this value is fixed at -3.5.

  From the sum of these terms obtained, a recurrence score (RS) is obtained.

  The relationship between recurrence score (RS) and risk of recurrence measures the expression of test and reference genes in biopsy tumor specimens from a population of lymph node negative patients with invasive breast cancer and applies this algorithm It is demanded by.

  It is known that the RS scale produced by the algorithm of the present invention can be adjusted in various ways. Thus, the RS scale specifically described above is effectively -4.5 to -2.0, but this range is, for example, 0-10, 0-50, or 0-100. Can be selected.

  For example, in a particular scaling approach, a scaled recurrence score (SRS) was calculated on a scale of 0-100. For convenience, 10 Ct units are added to each measured Ct value, and the unadjusted RS is calculated as described above. The scaled recurrence score (SRS) is calculated using the equation shown below.

  Patients assigned to various risk categories using the following recurrence scores:

  Using this adjusted approach, the recurrence score shown in FIG. 2 was calculated.

10. Utilization of Recurrence Score and Response Score to Treatment The recurrence score (RS) or response to treatment score (RTS) as determined by the algorithm of the present invention provides a useful tool for practitioners to make critical treatment decisions. Bring. Thus, if the RS of a particular patient is low, the physician may determine that chemotherapy after surgical resection of the cancer (eg, breast cancer) is not essential to ensure the patient's long-term survival. As a result, this patient does not suffer from the often very severe side effects of medical chemotherapy treatment standards. On the other hand, if it is determined that the RS is high, this information can be used to determine which chemotherapy or other treatment option (eg, radiation therapy) is best suited for the treatment of this patient. Similarly, if a patient's RTS score is low for a particular treatment, other more effective therapies are used to treat that particular patient's cancer.

  By way of example, current standard medical chemotherapy options for the treatment of breast cancer include anthracycline + cyclophosphamide (AC); or AC + taxane (ACT); or C + methotrexate + 5-fluorouracil (CMF); Alternatively, administration of any of these drugs alone or in any combination is included. Other chemotherapeutic agents often used for cancer (eg, breast cancer) treatment include, for example, Herceptin®, vinblastine, actinomycin D, etoposide, cisplatin, and doxorubicin. By determining the RS for a particular patient with the algorithm of the present invention, the physician can tailor the patient's treatment so that the patient has the best chance of long-term survival while minimizing undesirable side effects. is there.

  Thus, patients with a low risk of cancer recurrence typically receive hormonal treatment alone, or hormonal treatment and a less toxic chemotherapy treatment regimen (eg, using Herceptin®). On the other hand, patients who have been determined to have a moderate or high risk of cancer recurrence typically receive more aggressive chemotherapy (eg, anthracycline and / or taxane-based treatment regimens).

  RS values can also be used with therapeutic agents (eg, EGFR inhibitors) that are not currently required for major treatment protocols for specific cancers and / or by other treatment options, eg, radiation therapy It can also be used to determine whether a patient should be treated, alone or before or after chemotherapy treatment.

  Further details of the invention are provided in the following non-limiting examples.

Example 1
Algorithm for predicting recurrence of breast cancer This algorithm is based on tumor level measurements of 16 mRNA markers, which were selected by the following two major criteria: 1) The following clinical studies Univariate correlation with breast cancer recurrence, and 2) potential importance in tumor pathology as shown in the published scientific literature.

  These selectable markers indicate a role for several cellular effects and pathways in cancer recurrence: eg, Her2 growth factor; estrogen receptor, proliferation, and invasion. Consistent with current understanding of molecular pathology of breast cancer, genes that show increased mRNA levels in the growth factor axis, proliferation axis, and invasive axis correlate with increased risk of recurrence, whereas estrogen Genes that show increased mRNA levels in the receptor axis correlate with reduced risk of recurrence. Related genes in these pathways are not only related in biological function, but also in co-expression as shown by Pearson's correlation coefficient (data not shown). For calculating the Pearson correlation coefficient, see, for example, K.K. Pearson and A.M. See Lee, Biometrica 2: 357 (1902).

  An algorithm is constructed by weighting the contribution of specific co-expressed genes of the pathway. This model was iteratively optimized by selecting for the best correlation to recurrence risk. Model fitting involves selection of the best genes that exhibit the functional and co-expression axes described above, as well as selection for optimal equation coefficients.

  In addition, other genes were included that were not closely correlated in expression to any of the above genes or to each other but were also found to contribute independently to the prediction of cancer recurrence . Accumulated evidence in the biomedical literature that tumor macrophages provide a prognostic diagnosis of cancer (M. Orre and PA Rogers Gynecol. Oncol. 73: 47-50 [1999]; LM Coussens) et al., Cell 103: 481-90 [2000]; S. Huang et al., J. Natl. Cancer Inst. 94: 1134-42 [2002]; MA Cobleigh et al., Processed Sof A. S C.O.22: Abstract 3415 [2003]), the macrophage marker CD68 was also included in the test gene set. Finally, a model containing the following genes was identified: Grb7; Her2; ER; PR; BCl2; CEGP1; SURV; Ki-67; MYBL2; CCNB1; STK15; CTSL2;

With this algorithm, a recurrence risk score (RS) is obtained. Specifically, the RS is derived as follows:
1. GRB7 axis is defined GRB7 axis = (0.45-1.35) x GRB7 + (0.05-0.15) x HER2
2. GRB7 axis threshold is specified When GRB7 axis <−2 GRB7GT threshold = −2,
2. Other GRB7GT threshold = GRB7 axis Define ER axis Er axis = (EstR1 + PR + Bcl2 + CEGP1) / 4, where the individual contributions of the genes listed can be weighted by a conversion factor ranging from 0.5 to 1.5.
4). Proliferation axis defined Proliferation axis = (SURV + Ki.67 + MYBL2 + CCNB1 + STK15) / 5, where the individual contributions of the genes listed are weighted by a conversion factor from 0.5 to 1.5 obtain.
5. Define growth axis threshold (proliferation threshold) When growth axis <−3.5 Growth axis threshold = −3.5,
5. Other growth axis threshold = growth axis Define Invasion Axis Invasion Axis = (CTSL2 + STMY3) / 2, where the individual contributions of the listed genes can be weighted by a conversion factor from 0.5 to 1.5.
7). Calculate recurrence score (RS) RS = (0.23-0.70) × GRB7GT threshold− (0.17-0.51) × ER axis + (0.52-1.56) × proliferation axis threshold + ( 0.07 to 0.21) x invasive axis + (0.03 to 0.15) x CD68- (0.04 to 0.25) x GSTM1- (0.05 to 0.22) x BAG1.

In certain embodiments, RS is calculated as follows:
RS = 0.47 × GRB7GT threshold−0.34 × ER axis + 1.04 × proliferation axis threshold + 0.14 × invasive axis + 0.11 × CD68−0.17 × GSTM1-0.15BAG1.

  Those skilled in the art will appreciate that other genes can be used in place of the 16 test genes identified above. In general, any gene that is co-expressed with the genes in the 16-gene test panel (Pearson's correlation coefficient ≧ 0.40) can be used instead of the genes in the algorithm. To replace gene X with correlated gene Y, determine the range for gene X in the patient population, the range for gene Y in the patient population, and apply a linear transformation to convert the value for gene Y to gene X Maps to the corresponding value of. For example, if the value range for gene X in the patient population is 0-10 and the value range for gene Y in the patient population is 0-5, an appropriate conversion is to multiply the value of gene Y by 2.

  By way of illustration, the following genes within the Her2 amplicon are some of the genes that can be substituted into the growth factor gene subset: Q9BRT3; TCAP; PNMT; ML64; IPPD; Q9H7G1; Q9HBS1; CSF3.

  By way of illustration, some of the genes that can be replaced in a growth gene subset are: C20. orf1, TOP2A, CDC20, KNSL2, MELK, TK1, NEK2, LMNB1, PTTG1, BUB1, CCNE2, FLJ20354, MCM2, RAD54L, PR02000, PCNA, Chk1, NME1, TS, FOXM1, AD024, and HNRPA.

  By way of illustration, some of the genes that can be substituted into the estrogen receptor (ER) subset are: HNF3A, ErbB3, GATA3, BECN1, IGF1R, AKT2, DHPS, BAG1, hENT1, TOP2B, MDM2, CCND1, DKFZp586M0723 , NPD009, B.I. Catenin, IRS1, Bclx, RBM5, PTEN, A. Catenin, KRT18, ZNF217, ITGA7, GSN, MTA1, G. Catenin, DR5, RAD51C, BAD, TP53BP1, RIZ1, IGFBP2, RUNX1, PPM1D, TFF3, S100A8, P28, SFRS5, and IGFBP2.

  By way of illustration, some of the genes that can be replaced in the invasive axis are: upa, COL1A1, COL1A2, and TIMP2.

  Some of the genes that can be used in place of CD68 are: CTSB, CD18, CTSL, HLA. DPB1, MMP9.

  Some of the genes that can be used in place of GSTM1 are: GSTM3.2, MYH11.1, GSN. 3, ID 1.1.

  Some of the genes that can be used in place of BAG1 are: Bcl2.2, GATA3.3, DHPS. 3, HNF3A. 1.

  The determined RS value can be used to guide a two-term (yes or no) decision on whether to treat lymph node-negative breast cancer patients with adjuvant chemotherapy. For this reason, the threshold value may stipulate separating high-risk patients from low-risk patients. As previously mentioned, defining three categories of high risk, medium risk and low risk (requiring two RS cut points) may be more beneficial. Cut points can be selected to subgroup patients into the desired risk category by applying algorithms to specific clinical study data. For example, oncologists may wish to avoid treatment of patients with node-negative breast cancer who have a risk of recurrence of less than 10% within 10 years. By applying this algorithm to the clinical trial data described in Example 2, patients with RS less than -3.9 have a risk of recurrence of less than 10% in 10 years, compared to -3.5 Patients with large RS are shown to have a risk of recurrence greater than 39% over 10 years. Patients with moderate RS values can be considered to fall into the moderate risk category. Oncologists can obtain RS results in the form of serial number-related recurrence scores for 10-year Kaplan-Meier survival.

Example 2
Gene expression studies in 242 malignant breast tumors Gene expression studies are planned and executed to determine recurrence score (RS) values in a population of patients with node-negative invasive breast cancer and have RS values and disease Correlation between not surviving was determined. This study utilized stored fixed paraffin-embedded tumor blocks as a source of RNA and adapted stored patient records.

Research plan:
Molecular assays were performed on paraffin-embedded formalin-fixed primary breast tumor tissue obtained from 252 individual patients diagnosed with invasive breast cancer. All patients were lymph node negative, ER positive and treated with tamoxifen. The average age was 52 years and the average clinical tumor size was 2 cm. The average follow-up was 10.9 years. As of January 1, 2003, 41 patients relapsed locally or remotely or died of breast cancer. Patients are included in this study only if histopathological assessment (performed as described in the Materials and Methods section) shows adequate amount of tumor tissue and allogeneic pathology.

Materials and methods:
Each representative tumor block was characterized by standard histopathology for diagnosis, semi-quantitative assessment of tumor volume, and tumor progression. If the tumor area was less than 70% of the cross section, the tumor area was dissected with the naked eye and the tissue was removed from 6 (10 micron) sections. Otherwise, a total of 3 sections (also 10 microns thick each) were prepared. Sections were placed in two Costar Brand microcentrifuge tubes (polypropylene, 1.7 mL tubes, clear). When more than one tumor block was obtained as part of the surgical procedure, the most representative block of pathology was used for analysis. Gene expression analysis; mRNA was extracted and purified from fixed paraffin embedded tissue samples and prepared for gene expression analysis as described above. Molecular assays for quantitative gene expression were performed by RT-PCR using the ABI PRISM 7900 ^™ Sequence Detection System ^™ (Perkin-Elmer-Applied Biosystems, Foster City, CA, USA). The ABI PRISM 7900 ^™ consists of a thermocycler, laser, charge coupled device (CCD), camera and computer. This system amplifies samples in a 384 well format on a thermocycler. The system includes software for operating the instrument and analyzing the data.

result:
Normalized gene expression values were obtained from the above patient specimens and used to calculate the RS for each patient, which RS data for each Kaplan-Meier 10-year survival as shown in FIGS. 1A and B And adapted. As shown, patients with RS values less than about −3.75 have a ~ 90% chance of survival without recurrence for 10 years. The recurrence rate increases rapidly with RS values> -3.3. The risk of recurrence for 10 years with an RS of ˜−3.0 is about 30%, and the risk of recurrence for 10 years with an RS of ˜−2.0 is over 50%. FIG. 1B shows that approximately 70% of patients fall into the lowest risk category.

  Therefore, these results show a close agreement between the RS value as determined by the test and algorithm and the risk of breast cancer recurrence.

Example 3
Validity of the Algorithm of the Present Invention A fixed paraffin in patients enrolled in the tamoxifen single department of the National Surgical Adjuvant Breast and Bowel Project (NSABP) after conducting a prospective clinical validity study The performance of the multigene RT-PCR assay for RNA extraction and quantification from embedded tumor tissue was examined.

  Study B-14: A laboratory study to clinically examine whether genomic tumor expression profiles can clarify the likelihood of recurrence in patients with primary invasive breast cancer, negative axillary lymph nodes and estrogen receptor positive tumors . This NSABP Study B-14 was conducted to evaluate the efficacy of tamoxifen treatment after mastectomy in lymph node negative primary breast cancer patients and a total of 2,892 patients treated for 5 years as eligible Included. This protocol was randomized, double-blind, for a set of patients receiving tamoxifen twice daily at a dose of 10 mg and a set of control patients receiving physically identical placebo tablets. Carried out. Multigene assays were compared against standard histopathological and molecular criteria for tumor classification.

  Patient-specific recurrence scores derived from multigene assays were obtained for a total of 668 eligible patients (mean follow-up period, 14.1 years) from the tamoxifen single department of NSABP Study B-14. By statistical analysis, the patient-specific recurrence score obtained (FIG. 2) was significantly (P = 6.5 E-9) correlated with the likelihood of distant recurrence and tumor classification (patients at surgery) It has been shown to provide important information beyond standard clinical criteria (including age, clinical tumor size and tumor progression).

  It has been found that the assays and algorithms of the present invention provide reproducible and useful criteria for the likelihood of distant recurrence from tumor tissue collected at the time of surgery for node-negative, ER +, primary breast cancer patients treated with tamoxifen. It was done. The patient recurrence score provided by the method of the present invention is significant (P <<) over the standard histopathological and molecular criteria of tumor classification commonly used in clinical practice, including age, clinical tumor size and tumor progression. 0.0001) brings information. In contrast to other common clinical criteria, this patient recurrence score is highly reproducible, and unlike other molecular tests, information can be shared simultaneously across multiple genomic markers (including ER, PR and HER2) use.

  Although the present invention has been described with reference to what are considered to be the specific embodiments, it should be understood that the invention is not limited to such embodiments. On the contrary, the invention is intended to cover various modifications and equivalents falling within the spirit and scope of the appended claims. For example, while the present disclosure focuses on the identification of various breast cancer-related genes and gene sets, and personal prognosis of breast cancer, similar genes, gene sets and methods for other types of cancer are described in detail. Within the scope of this specification.

  All references cited throughout this disclosure are expressly incorporated herein by reference.

  Table 1 Table 1 lists test and reference genes, and the expression of these genes is used to construct an algorithm for breast cancer recurrence. This table contains the gene accession numbers, the forward and reverse primers used for PCR amplification (referred to as “f” and “r”, respectively) and the probe (referred to as “p”) sequences. .

Table 2 Table 2 shows the amplicon sequences of the aforementioned genes.
FIG. 1A shows the results of a clinical study of 242 patients with invasive lymph node-negative breast cancer for which 10-year recurrence data is available. FIG. 1A shows the proportion of patients who have actually relapsed cancer (Kaplan-Meier method) to the calculated relapse score. Patients are grouped in 5th percentile units, except for patients with scores less than -4.5 (grouped together). FIG. 1B shows the results of a clinical study of 242 patients with invasive lymph node negative breast cancer for which 10-year recurrence data is available. FIG. 1B is similar to FIG. 1A. FIG. 1B also shows the percentage of patients who have actually relapsed cancer, in which case the actual relapse data is plotted against the percentile of the recurrence score. Patients are grouped in 5th percentile units, except the lowest 20th percentile (grouped together). FIG. 2 illustrates the relationship between the recurrence score {RS} and the rate or likelihood of distant recurrence of breast cancer after 10 years. Data is acquired from the NSABP B-14 patient population. The dashed line represents the 95% confidence interval. Each check mark on the X axis indicates a patient with a matching RS.

Claims (12)

A method of determining the likelihood of recurrence of breast cancer or response to tamoxifen therapy in a human patient with invasive lymph node negative estrogen receptor (ER) positive breast cancer , the method comprising the following steps:
(A) In a biological sample obtained from the breast cancer tumor of the breast, GRB7; HER2; EstR1; PR; BCL2; CEGP1; SURV; 67; MYBL2; CCNB1; STK15; measuring the expression level of an RNA transcript of a gene group consisting of CTSL2, STMY3, CD68, GSTM1 and BAG1, or an expression product thereof;
(B) The following gene subset:
(I) Proliferation subset: SURV, Ki. 67, MYBL2, CCNB1, and STK15;
(Ii) Invasive subset: CTSL2, and STMY3;
(Iii) Growth factor subset: GRB7 and HER2;
(Iv) an estrogen receptor subsets: EstR1, PR, B CL 2 , and CEGP1;
Categorizing into:
(C) multiplying the expression level of the RNA transcript of each gene obtained in (a) or their expression product by the contribution rate of the gene to breast cancer recurrence;
(D) adding the values obtained in (c) for each subset, and calculating each score including a proliferation score, an invasive score, a GRB7 score, and an ER score;
(E) The score of each subset obtained in (d) is weighted and added to the contribution rate of breast cancer recurrence of the subset, and the recurrence score is added by adding the weighted expression levels of CD68, GSTM1 and BAG1. step for calculating a (RS); and
(F) creating a report providing the RS, wherein a higher RS indicates that the patient is more likely to have breast cancer recurrence or the patient is less responsive to hormonal therapy ,Method. The genes GRB7, HER2, EstR1, PR, CEGP1, BCL2, SURV, Ki. The method of claim 1, wherein the expression of 67, MYBL2, CCNB1, STK15, CTSL2, STMY3, CD68, GSTM1 and BAG1 is at the protein expression level. 2. The method of claim 1 , wherein an increase in the level of CD68 RNA transcript, or expression product thereof, is associated with an increased risk of breast cancer recurrence and is given a positive value. GSTM1 and RNA transcripts BAG 1, or an increase in the level of its expression product, associated with reduced risk of recurrence of breast cancer, and a negative value is applied, the method of claim 1. The method of claim 1, wherein individual contributions of each gene of each gene subset of (i) to (iv) are weighted separately. The method according to claim 1, comprising the step of equally weighting individual contributions of the genes described in the gene subsets of (i) to (iv), or their expression products, to breast cancer recurrence. Increased levels of RNA transcripts of the genes in subsets (i), (ii) and (iii), or their expression products, are associated with an increased risk of breast cancer recurrence and are given positive values; The method of claim 1. 2. The increase in the level of gene RNA transcripts, or their expression products in the subset (iv) genes is associated with a reduced risk of breast cancer recurrence and is given a negative value. Method. The method of claim 1, wherein the biological sample is selected from the group consisting of fresh tumor tissue, fine needle aspirate, ascites, tubal wash and pleural fluid. The method of claim 1, wherein the biological sample is obtained from fixed paraffin-embedded tumor tissue. The method of claim 1, wherein the expression level of the RNA transcript is determined by RT-PCR. The method of claim 1, wherein the biological sample comprises fragmented RNA.

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